The present invention relates to software, and more particularly, to business management software.
Marketers/advertisers have difficulties in reaching their target audiences. In today's multi-channel environment, it's becoming increasingly difficult for advertisers to target audiences. An appropriate accounting mechanism is needed that can ensure a proper compensation for reaching the target audience. Marketers have difficulties in reaching large blocks of unduplicated eyeballs. They are willing to pay top dollar for that (an issue that is called the reach premium).
Conventional data that advertisers seek to collect for making future advertising decisions include receivers' liking of the advertisement, preference for the advertisement and intent to purchase based on the advertisement. However, to date, such conventional data has only been measured by direct responses to advertising methods, such as coupons, toll-free phone numbers, and/or copy testing with sample receivers. As a consequence, the collection methods of such conventional data have merely provided gross indicators of an advertisement's audience and financial impact.
Traditionally, the cost for an advertiser to run an advertisement (e.g. commercial, etc.) has been based on an estimated number of people who are predicted to watch the content that is broadcasted around the advertisement itself, such as a television show, etc. In the case of a commercial on television, the estimated cost is adjusted after the commercial is presented, according to information collected from a sample of viewers/receivers that only measure the percentage of the sample viewers/receivers whose television/set-top box was tuned on the proper channel when the specific commercial was broadcasted and presented. On the web, the estimated cost is adjusted according to the number of viewers who clicked on the advertisement. In addition, the cost has also been associated with a type of demographic group of the predicted number of receivers. The advertisement cost is also associated with the type of content around it. To this end, the cost is not based on usage and/or effectiveness of the actual advertisement, and less attention is paid to the advertisement.
Recent changes in the ways in which receivers watch content, and especially television, have affected the accuracy of the traditional cost calculation method. For example, with innovations such as digital cable television which includes a wide variety of channels, many receivers are no longer watching advertisements between television shows, but are rather simply switching the channel during the commercial break.
In addition, with the large number of television channels from which to choose, the number of receivers for particular channels has declined in general. This widespread disbursement has resulted in narrow demographics associated with each channel which, in turn, has made it difficult for advertisers to successfully target broad demographics.
Furthermore, accurately determining a demographic group generally associated with a specific channel by taking a sample of receivers associated with the specific channel is difficult. This is mainly due to the fact that many receivers do not only watch specific channels, but instead disburse their viewing throughout many channels. Thus, one may find only a few households (if any) that watch specific channels, and this is not a big enough sample to make the appropriate statistical calculations for those channels and the associated content, advertisement, etc. Hence, the cost of advertisement in these channels can not be feasibly calculated using traditional sampling methods.
Another inherent problem with traditional advertisement pricing methods is that, since information is typically provided manually, they are incapable of allowing even near real-time calculation. Also, information that is capable of being collected automatically still must be processed by a third party (e.g. Nielsen, etc.), thus delaying the distribution of the information. As a consequence, the pricing of advertisements are also incapable of being calculated in real-time or near real time.
These problems result in advertisers inefficiently spending money on advertising. For these reasons, there is a need for a usage-sensitive method of determining costs to advertisers for running advertisements. There is thus a need for overcoming these and/or other problems associated with the prior art.